Overview

Dataset statistics

Number of variables27
Number of observations39
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.5 KiB
Average record size in memory275.8 B

Variable types

Categorical8
Numeric19

Warnings

United Kingdom has constant value "0" Constant
Greece is highly correlated with IcelandHigh correlation
Iceland is highly correlated with Greece and 1 other fieldsHigh correlation
Moldova is highly correlated with Iceland and 1 other fieldsHigh correlation
Switzerland is highly correlated with MoldovaHigh correlation
Greece is highly correlated with IcelandHigh correlation
Iceland is highly correlated with Greece and 1 other fieldsHigh correlation
Moldova is highly correlated with IcelandHigh correlation
Belgium is highly correlated with Ukraine and 3 other fieldsHigh correlation
Israel is highly correlated with Norway and 5 other fieldsHigh correlation
Greece is highly correlated with Country (Voters (vertical), Finalists (horizontal)) and 4 other fieldsHigh correlation
Ukraine is highly correlated with Belgium and 4 other fieldsHigh correlation
Norway is highly correlated with Israel and 2 other fieldsHigh correlation
Albania is highly correlated with Norway and 5 other fieldsHigh correlation
France is highly correlated with Country (Voters (vertical), Finalists (horizontal)) and 4 other fieldsHigh correlation
Lithuania is highly correlated with Country (Voters (vertical), Finalists (horizontal))High correlation
Country (Voters (vertical), Finalists (horizontal)) is highly correlated with Belgium and 24 other fieldsHigh correlation
Bulgaria is highly correlated with Albania and 5 other fieldsHigh correlation
Switzerland is highly correlated with Belgium and 5 other fieldsHigh correlation
Germany is highly correlated with France and 5 other fieldsHigh correlation
Iceland is highly correlated with Israel and 4 other fieldsHigh correlation
Russia is highly correlated with Greece and 4 other fieldsHigh correlation
Italy is highly correlated with Ukraine and 4 other fieldsHigh correlation
Malta is highly correlated with Belgium and 6 other fieldsHigh correlation
Moldova is highly correlated with Greece and 9 other fieldsHigh correlation
Finland is highly correlated with Greece and 3 other fieldsHigh correlation
Sweden is highly correlated with Israel and 6 other fieldsHigh correlation
Netherlands is highly correlated with Ukraine and 1 other fieldsHigh correlation
Serbia is highly correlated with Country (Voters (vertical), Finalists (horizontal)) and 2 other fieldsHigh correlation
Spain is highly correlated with Israel and 3 other fieldsHigh correlation
San Marino is highly correlated with Israel and 6 other fieldsHigh correlation
Cyprus is highly correlated with France and 4 other fieldsHigh correlation
Portugal is highly correlated with Country (Voters (vertical), Finalists (horizontal)) and 3 other fieldsHigh correlation
Azerbaijan is highly correlated with France and 3 other fieldsHigh correlation
Albania is highly correlated with Country (Voters (vertical), Finalists (horizontal)) and 1 other fieldsHigh correlation
Country (Voters (vertical), Finalists (horizontal)) is highly correlated with Albania and 6 other fieldsHigh correlation
Netherlands is highly correlated with Country (Voters (vertical), Finalists (horizontal)) and 1 other fieldsHigh correlation
Germany is highly correlated with Country (Voters (vertical), Finalists (horizontal)) and 1 other fieldsHigh correlation
Serbia is highly correlated with Country (Voters (vertical), Finalists (horizontal)) and 1 other fieldsHigh correlation
Spain is highly correlated with Country (Voters (vertical), Finalists (horizontal)) and 1 other fieldsHigh correlation
United Kingdom is highly correlated with Albania and 6 other fieldsHigh correlation
Norway is highly correlated with Country (Voters (vertical), Finalists (horizontal)) and 1 other fieldsHigh correlation
Country (Voters (vertical), Finalists (horizontal)) is uniformly distributed Uniform
Country (Voters (vertical), Finalists (horizontal)) has unique values Unique
Azerbaijan has 30 (76.9%) zeros Zeros
Belgium has 22 (56.4%) zeros Zeros
Bulgaria has 13 (33.3%) zeros Zeros
Cyprus has 28 (71.8%) zeros Zeros
Finland has 20 (51.3%) zeros Zeros
France has 6 (15.4%) zeros Zeros
Greece has 24 (61.5%) zeros Zeros
Iceland has 11 (28.2%) zeros Zeros
Israel has 22 (56.4%) zeros Zeros
Italy has 11 (28.2%) zeros Zeros
Lithuania has 27 (69.2%) zeros Zeros
Malta has 4 (10.3%) zeros Zeros
Moldova has 33 (84.6%) zeros Zeros
Portugal has 17 (43.6%) zeros Zeros
Russia has 18 (46.2%) zeros Zeros
San Marino has 32 (82.1%) zeros Zeros
Sweden has 29 (74.4%) zeros Zeros
Switzerland has 5 (12.8%) zeros Zeros
Ukraine has 20 (51.3%) zeros Zeros

Reproduction

Analysis started2021-05-30 15:58:27.615365
Analysis finished2021-05-30 16:01:03.102447
Duration2 minutes and 35.49 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Country (Voters (vertical), Finalists (horizontal))
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
Italy
 
1
Greece
 
1
Ukraine
 
1
Bulgaria
 
1
Israel
 
1
Other values (34)
34 

Length

Max length15
Median length7
Mean length7.512820513
Min length5

Characters and Unicode

Total characters293
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)100.0%

Sample

1st rowAlbania
2nd rowAustralia
3rd rowAustria
4th rowAzerbaijan
5th rowBelgium

Common Values

ValueCountFrequency (%)
Italy1
 
2.6%
Greece1
 
2.6%
Ukraine1
 
2.6%
Bulgaria1
 
2.6%
Israel1
 
2.6%
Germany1
 
2.6%
Australia1
 
2.6%
Slovenia1
 
2.6%
Switzerland1
 
2.6%
Austria1
 
2.6%
Other values (29)29
74.4%

Length

2021-05-30T21:31:03.585685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
czechia1
 
2.4%
sweden1
 
2.4%
azerbaijan1
 
2.4%
north1
 
2.4%
estonia1
 
2.4%
russia1
 
2.4%
latvia1
 
2.4%
spain1
 
2.4%
greece1
 
2.4%
australia1
 
2.4%
Other values (32)32
76.2%

Most occurring characters

ValueCountFrequency (%)
a45
15.4%
i25
 
8.5%
n24
 
8.2%
e23
 
7.8%
r21
 
7.2%
l16
 
5.5%
o14
 
4.8%
t13
 
4.4%
d11
 
3.8%
u8
 
2.7%
Other values (30)93
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter248
84.6%
Uppercase Letter42
 
14.3%
Space Separator3
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a45
18.1%
i25
10.1%
n24
9.7%
e23
9.3%
r21
8.5%
l16
 
6.5%
o14
 
5.6%
t13
 
5.2%
d11
 
4.4%
u8
 
3.2%
Other values (13)48
19.4%
Uppercase Letter
ValueCountFrequency (%)
S6
14.3%
A4
9.5%
I4
9.5%
M4
9.5%
C3
 
7.1%
G3
 
7.1%
N3
 
7.1%
B2
 
4.8%
F2
 
4.8%
L2
 
4.8%
Other values (6)9
21.4%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin290
99.0%
Common3
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a45
15.5%
i25
 
8.6%
n24
 
8.3%
e23
 
7.9%
r21
 
7.2%
l16
 
5.5%
o14
 
4.8%
t13
 
4.5%
d11
 
3.8%
u8
 
2.8%
Other values (29)90
31.0%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a45
15.4%
i25
 
8.5%
n24
 
8.2%
e23
 
7.8%
r21
 
7.2%
l16
 
5.5%
o14
 
4.8%
t13
 
4.4%
d11
 
3.8%
u8
 
2.7%
Other values (30)93
31.7%

Albania
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
0
35 
7
 
1
1
 
1
12
 
1
2
 
1

Length

Max length2
Median length1
Mean length1.025641026
Min length1

Characters and Unicode

Total characters40
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)10.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
035
89.7%
71
 
2.6%
11
 
2.6%
121
 
2.6%
21
 
2.6%

Length

2021-05-30T21:31:03.998015image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-30T21:31:04.232192image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
035
89.7%
71
 
2.6%
11
 
2.6%
121
 
2.6%
21
 
2.6%

Most occurring characters

ValueCountFrequency (%)
035
87.5%
12
 
5.0%
22
 
5.0%
71
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number40
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
035
87.5%
12
 
5.0%
22
 
5.0%
71
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common40
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
035
87.5%
12
 
5.0%
22
 
5.0%
71
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII40
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
035
87.5%
12
 
5.0%
22
 
5.0%
71
 
2.5%

Azerbaijan
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8205128205
Minimum0
Maximum8
Zeros30
Zeros (%)76.9%
Negative0
Negative (%)0.0%
Memory size440.0 B
2021-05-30T21:31:04.377052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.1
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.833486682
Coefficient of variation (CV)2.234561894
Kurtosis6.913746205
Mean0.8205128205
Median Absolute Deviation (MAD)0
Skewness2.628250023
Sum32
Variance3.361673414
MonotonicityNot monotonic
2021-05-30T21:31:04.517645image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
030
76.9%
25
 
12.8%
31
 
2.6%
51
 
2.6%
61
 
2.6%
81
 
2.6%
ValueCountFrequency (%)
030
76.9%
25
 
12.8%
31
 
2.6%
51
 
2.6%
61
 
2.6%
81
 
2.6%
ValueCountFrequency (%)
81
 
2.6%
61
 
2.6%
51
 
2.6%
31
 
2.6%
25
 
12.8%
030
76.9%

Belgium
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.820512821
Minimum0
Maximum7
Zeros22
Zeros (%)56.4%
Negative0
Negative (%)0.0%
Memory size440.0 B
2021-05-30T21:31:04.673864image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.41564849
Coefficient of variation (CV)1.326905508
Kurtosis-0.7844709133
Mean1.820512821
Median Absolute Deviation (MAD)0
Skewness0.8946069093
Sum71
Variance5.835357625
MonotonicityNot monotonic
2021-05-30T21:31:04.847214image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
022
56.4%
36
 
15.4%
65
 
12.8%
12
 
5.1%
52
 
5.1%
41
 
2.6%
71
 
2.6%
ValueCountFrequency (%)
022
56.4%
12
 
5.1%
36
 
15.4%
41
 
2.6%
52
 
5.1%
65
 
12.8%
71
 
2.6%
ValueCountFrequency (%)
71
 
2.6%
65
 
12.8%
52
 
5.1%
41
 
2.6%
36
 
15.4%
12
 
5.1%
022
56.4%

Bulgaria
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.58974359
Minimum0
Maximum12
Zeros13
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size440.0 B
2021-05-30T21:31:04.987802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q36
95-th percentile10.2
Maximum12
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.632586064
Coefficient of variation (CV)1.011934689
Kurtosis-0.3014629518
Mean3.58974359
Median Absolute Deviation (MAD)3
Skewness0.7625982933
Sum140
Variance13.19568151
MonotonicityNot monotonic
2021-05-30T21:31:05.159603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
013
33.3%
66
15.4%
54
 
10.3%
13
 
7.7%
23
 
7.7%
43
 
7.7%
82
 
5.1%
102
 
5.1%
122
 
5.1%
31
 
2.6%
ValueCountFrequency (%)
013
33.3%
13
 
7.7%
23
 
7.7%
31
 
2.6%
43
 
7.7%
54
 
10.3%
66
15.4%
82
 
5.1%
102
 
5.1%
122
 
5.1%
ValueCountFrequency (%)
122
 
5.1%
102
 
5.1%
82
 
5.1%
66
15.4%
54
 
10.3%
43
 
7.7%
31
 
2.6%
23
 
7.7%
13
 
7.7%
013
33.3%

Cyprus
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.282051282
Minimum0
Maximum12
Zeros28
Zeros (%)71.8%
Negative0
Negative (%)0.0%
Memory size440.0 B
2021-05-30T21:31:05.334041image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.5
95-th percentile7
Maximum12
Range12
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation2.655170903
Coefficient of variation (CV)2.071033304
Kurtosis6.68423438
Mean1.282051282
Median Absolute Deviation (MAD)0
Skewness2.515341235
Sum50
Variance7.049932524
MonotonicityNot monotonic
2021-05-30T21:31:05.537116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
028
71.8%
23
 
7.7%
42
 
5.1%
72
 
5.1%
11
 
2.6%
31
 
2.6%
61
 
2.6%
121
 
2.6%
ValueCountFrequency (%)
028
71.8%
11
 
2.6%
23
 
7.7%
31
 
2.6%
42
 
5.1%
61
 
2.6%
72
 
5.1%
121
 
2.6%
ValueCountFrequency (%)
121
 
2.6%
72
 
5.1%
61
 
2.6%
42
 
5.1%
31
 
2.6%
23
 
7.7%
11
 
2.6%
028
71.8%

Finland
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.128205128
Minimum0
Maximum10
Zeros20
Zeros (%)51.3%
Negative0
Negative (%)0.0%
Memory size440.0 B
2021-05-30T21:31:05.708916image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.5
95-th percentile8.2
Maximum10
Range10
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation3.113463862
Coefficient of variation (CV)1.462952899
Kurtosis0.7929404852
Mean2.128205128
Median Absolute Deviation (MAD)0
Skewness1.418344186
Sum83
Variance9.69365722
MonotonicityNot monotonic
2021-05-30T21:31:05.944386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
020
51.3%
15
 
12.8%
43
 
7.7%
83
 
7.7%
22
 
5.1%
32
 
5.1%
102
 
5.1%
51
 
2.6%
71
 
2.6%
ValueCountFrequency (%)
020
51.3%
15
 
12.8%
22
 
5.1%
32
 
5.1%
43
 
7.7%
51
 
2.6%
71
 
2.6%
83
 
7.7%
102
 
5.1%
ValueCountFrequency (%)
102
 
5.1%
83
 
7.7%
71
 
2.6%
51
 
2.6%
43
 
7.7%
32
 
5.1%
22
 
5.1%
15
 
12.8%
020
51.3%

France
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.358974359
Minimum0
Maximum12
Zeros6
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size440.0 B
2021-05-30T21:31:06.163083image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.5
median6
Q310
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation4.094201159
Coefficient of variation (CV)0.64384615
Kurtosis-1.112957507
Mean6.358974359
Median Absolute Deviation (MAD)3
Skewness-0.06376964086
Sum248
Variance16.76248313
MonotonicityNot monotonic
2021-05-30T21:31:06.398549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
128
20.5%
06
15.4%
75
12.8%
44
10.3%
64
10.3%
104
10.3%
33
 
7.7%
52
 
5.1%
82
 
5.1%
21
 
2.6%
ValueCountFrequency (%)
06
15.4%
21
 
2.6%
33
 
7.7%
44
10.3%
52
 
5.1%
64
10.3%
75
12.8%
82
 
5.1%
104
10.3%
128
20.5%
ValueCountFrequency (%)
128
20.5%
104
10.3%
82
 
5.1%
75
12.8%
64
10.3%
52
 
5.1%
44
10.3%
33
 
7.7%
21
 
2.6%
06
15.4%

Germany
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
0
37 
1
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)5.1%

Sample

1st row0
2nd row0
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
037
94.9%
11
 
2.6%
21
 
2.6%

Length

2021-05-30T21:31:06.900546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-30T21:31:07.056756image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
037
94.9%
11
 
2.6%
21
 
2.6%

Most occurring characters

ValueCountFrequency (%)
037
94.9%
21
 
2.6%
11
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number39
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
037
94.9%
21
 
2.6%
11
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common39
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
037
94.9%
21
 
2.6%
11
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII39
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
037
94.9%
21
 
2.6%
11
 
2.6%

Greece
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.333333333
Minimum0
Maximum12
Zeros24
Zeros (%)61.5%
Negative0
Negative (%)0.0%
Memory size440.0 B
2021-05-30T21:31:07.181729image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.5
95-th percentile10.2
Maximum12
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation3.729916983
Coefficient of variation (CV)1.59853585
Kurtosis0.8199676947
Mean2.333333333
Median Absolute Deviation (MAD)0
Skewness1.442669314
Sum91
Variance13.9122807
MonotonicityNot monotonic
2021-05-30T21:31:07.353599image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
024
61.5%
83
 
7.7%
12
 
5.1%
32
 
5.1%
62
 
5.1%
122
 
5.1%
21
 
2.6%
41
 
2.6%
71
 
2.6%
101
 
2.6%
ValueCountFrequency (%)
024
61.5%
12
 
5.1%
21
 
2.6%
32
 
5.1%
41
 
2.6%
62
 
5.1%
71
 
2.6%
83
 
7.7%
101
 
2.6%
122
 
5.1%
ValueCountFrequency (%)
122
 
5.1%
101
 
2.6%
83
 
7.7%
71
 
2.6%
62
 
5.1%
41
 
2.6%
32
 
5.1%
21
 
2.6%
12
 
5.1%
024
61.5%

Iceland
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.076923077
Minimum0
Maximum12
Zeros11
Zeros (%)28.2%
Negative0
Negative (%)0.0%
Memory size440.0 B
2021-05-30T21:31:07.513376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q38
95-th percentile10
Maximum12
Range12
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3.969519495
Coefficient of variation (CV)0.781875052
Kurtosis-1.462345627
Mean5.076923077
Median Absolute Deviation (MAD)3
Skewness-0.05995521357
Sum198
Variance15.75708502
MonotonicityNot monotonic
2021-05-30T21:31:07.685205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
011
28.2%
87
17.9%
107
17.9%
33
 
7.7%
43
 
7.7%
73
 
7.7%
52
 
5.1%
21
 
2.6%
61
 
2.6%
121
 
2.6%
ValueCountFrequency (%)
011
28.2%
21
 
2.6%
33
 
7.7%
43
 
7.7%
52
 
5.1%
61
 
2.6%
73
 
7.7%
87
17.9%
107
17.9%
121
 
2.6%
ValueCountFrequency (%)
121
 
2.6%
107
17.9%
87
17.9%
73
 
7.7%
61
 
2.6%
52
 
5.1%
43
 
7.7%
33
 
7.7%
21
 
2.6%
011
28.2%

Israel
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.871794872
Minimum0
Maximum8
Zeros22
Zeros (%)56.4%
Negative0
Negative (%)0.0%
Memory size440.0 B
2021-05-30T21:31:07.841419image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile7.1
Maximum8
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.617546935
Coefficient of variation (CV)1.398415486
Kurtosis-0.155325655
Mean1.871794872
Median Absolute Deviation (MAD)0
Skewness1.10311177
Sum73
Variance6.851551957
MonotonicityNot monotonic
2021-05-30T21:31:08.014801image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
022
56.4%
13
 
7.7%
43
 
7.7%
53
 
7.7%
32
 
5.1%
62
 
5.1%
82
 
5.1%
21
 
2.6%
71
 
2.6%
ValueCountFrequency (%)
022
56.4%
13
 
7.7%
21
 
2.6%
32
 
5.1%
43
 
7.7%
53
 
7.7%
62
 
5.1%
71
 
2.6%
82
 
5.1%
ValueCountFrequency (%)
82
 
5.1%
71
 
2.6%
62
 
5.1%
53
 
7.7%
43
 
7.7%
32
 
5.1%
21
 
2.6%
13
 
7.7%
022
56.4%

Italy
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.282051282
Minimum0
Maximum12
Zeros11
Zeros (%)28.2%
Negative0
Negative (%)0.0%
Memory size440.0 B
2021-05-30T21:31:08.171015image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q39
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.248521216
Coefficient of variation (CV)0.8043316865
Kurtosis-1.349525721
Mean5.282051282
Median Absolute Deviation (MAD)4
Skewness0.08455082259
Sum206
Variance18.04993252
MonotonicityNot monotonic
2021-05-30T21:31:08.327233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
011
28.2%
106
15.4%
65
12.8%
84
 
10.3%
124
 
10.3%
33
 
7.7%
42
 
5.1%
52
 
5.1%
21
 
2.6%
71
 
2.6%
ValueCountFrequency (%)
011
28.2%
21
 
2.6%
33
 
7.7%
42
 
5.1%
52
 
5.1%
65
12.8%
71
 
2.6%
84
 
10.3%
106
15.4%
124
 
10.3%
ValueCountFrequency (%)
124
 
10.3%
106
15.4%
84
 
10.3%
71
 
2.6%
65
12.8%
52
 
5.1%
42
 
5.1%
33
 
7.7%
21
 
2.6%
011
28.2%

Lithuania
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.41025641
Minimum0
Maximum12
Zeros27
Zeros (%)69.2%
Negative0
Negative (%)0.0%
Memory size440.0 B
2021-05-30T21:31:08.532782image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile6.4
Maximum12
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.816473614
Coefficient of variation (CV)1.997135835
Kurtosis6.111380468
Mean1.41025641
Median Absolute Deviation (MAD)0
Skewness2.461124479
Sum55
Variance7.932523617
MonotonicityNot monotonic
2021-05-30T21:31:08.689001image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
027
69.2%
23
 
7.7%
32
 
5.1%
42
 
5.1%
62
 
5.1%
11
 
2.6%
101
 
2.6%
121
 
2.6%
ValueCountFrequency (%)
027
69.2%
11
 
2.6%
23
 
7.7%
32
 
5.1%
42
 
5.1%
62
 
5.1%
101
 
2.6%
121
 
2.6%
ValueCountFrequency (%)
121
 
2.6%
101
 
2.6%
62
 
5.1%
42
 
5.1%
32
 
5.1%
23
 
7.7%
11
 
2.6%
027
69.2%

Malta
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.333333333
Minimum0
Maximum12
Zeros4
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size440.0 B
2021-05-30T21:31:08.876451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q37
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.55656053
Coefficient of variation (CV)0.6668550995
Kurtosis-0.5759857341
Mean5.333333333
Median Absolute Deviation (MAD)2
Skewness0.2878738469
Sum208
Variance12.64912281
MonotonicityNot monotonic
2021-05-30T21:31:09.079414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
56
15.4%
76
15.4%
45
12.8%
04
10.3%
14
10.3%
124
10.3%
83
7.7%
32
 
5.1%
62
 
5.1%
102
 
5.1%
ValueCountFrequency (%)
04
10.3%
14
10.3%
21
 
2.6%
32
 
5.1%
45
12.8%
56
15.4%
62
 
5.1%
76
15.4%
83
7.7%
102
 
5.1%
ValueCountFrequency (%)
124
10.3%
102
 
5.1%
83
7.7%
76
15.4%
62
 
5.1%
56
15.4%
45
12.8%
32
 
5.1%
21
 
2.6%
14
10.3%

Moldova
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.358974359
Minimum0
Maximum12
Zeros33
Zeros (%)84.6%
Negative0
Negative (%)0.0%
Memory size440.0 B
2021-05-30T21:31:09.220011image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10.2
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.406550806
Coefficient of variation (CV)2.506707197
Kurtosis4.509724696
Mean1.358974359
Median Absolute Deviation (MAD)0
Skewness2.395020275
Sum53
Variance11.60458839
MonotonicityNot monotonic
2021-05-30T21:31:09.376224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
033
84.6%
122
 
5.1%
51
 
2.6%
61
 
2.6%
81
 
2.6%
101
 
2.6%
ValueCountFrequency (%)
033
84.6%
51
 
2.6%
61
 
2.6%
81
 
2.6%
101
 
2.6%
122
 
5.1%
ValueCountFrequency (%)
122
 
5.1%
101
 
2.6%
81
 
2.6%
61
 
2.6%
51
 
2.6%
033
84.6%

Netherlands
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
0
34 
3
 
2
2
 
2
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.6%

Sample

1st row0
2nd row3
3rd row3
4th row0
5th row0

Common Values

ValueCountFrequency (%)
034
87.2%
32
 
5.1%
22
 
5.1%
11
 
2.6%

Length

2021-05-30T21:31:09.754701image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-30T21:31:09.864054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
034
87.2%
32
 
5.1%
22
 
5.1%
11
 
2.6%

Most occurring characters

ValueCountFrequency (%)
034
87.2%
32
 
5.1%
22
 
5.1%
11
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number39
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
034
87.2%
32
 
5.1%
22
 
5.1%
11
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common39
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
034
87.2%
32
 
5.1%
22
 
5.1%
11
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII39
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
034
87.2%
32
 
5.1%
22
 
5.1%
11
 
2.6%

Norway
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
0
34 
2
 
2
3
 
1
7
 
1
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)7.7%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
034
87.2%
22
 
5.1%
31
 
2.6%
71
 
2.6%
11
 
2.6%

Length

2021-05-30T21:31:10.224136image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-30T21:31:10.349105image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
034
87.2%
22
 
5.1%
31
 
2.6%
71
 
2.6%
11
 
2.6%

Most occurring characters

ValueCountFrequency (%)
034
87.2%
22
 
5.1%
31
 
2.6%
71
 
2.6%
11
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number39
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
034
87.2%
22
 
5.1%
31
 
2.6%
71
 
2.6%
11
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common39
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
034
87.2%
22
 
5.1%
31
 
2.6%
71
 
2.6%
11
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII39
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
034
87.2%
22
 
5.1%
31
 
2.6%
71
 
2.6%
11
 
2.6%

Portugal
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.230769231
Minimum0
Maximum12
Zeros17
Zeros (%)43.6%
Negative0
Negative (%)0.0%
Memory size440.0 B
2021-05-30T21:31:10.458454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36.5
95-th percentile10
Maximum12
Range12
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation3.709600195
Coefficient of variation (CV)1.148209584
Kurtosis-0.8741908984
Mean3.230769231
Median Absolute Deviation (MAD)1
Skewness0.7077415858
Sum126
Variance13.7611336
MonotonicityNot monotonic
2021-05-30T21:31:10.649452image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
017
43.6%
74
 
10.3%
13
 
7.7%
23
 
7.7%
53
 
7.7%
63
 
7.7%
83
 
7.7%
102
 
5.1%
121
 
2.6%
ValueCountFrequency (%)
017
43.6%
13
 
7.7%
23
 
7.7%
53
 
7.7%
63
 
7.7%
74
 
10.3%
83
 
7.7%
102
 
5.1%
121
 
2.6%
ValueCountFrequency (%)
121
 
2.6%
102
 
5.1%
83
 
7.7%
74
 
10.3%
63
 
7.7%
53
 
7.7%
23
 
7.7%
13
 
7.7%
017
43.6%

Russia
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.666666667
Minimum0
Maximum12
Zeros18
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size440.0 B
2021-05-30T21:31:10.826572image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile10
Maximum12
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.622541514
Coefficient of variation (CV)1.358453068
Kurtosis0.2662431513
Mean2.666666667
Median Absolute Deviation (MAD)1
Skewness1.250500475
Sum104
Variance13.12280702
MonotonicityNot monotonic
2021-05-30T21:31:10.982782image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
018
46.2%
14
 
10.3%
24
 
10.3%
103
 
7.7%
32
 
5.1%
42
 
5.1%
72
 
5.1%
82
 
5.1%
61
 
2.6%
121
 
2.6%
ValueCountFrequency (%)
018
46.2%
14
 
10.3%
24
 
10.3%
32
 
5.1%
42
 
5.1%
61
 
2.6%
72
 
5.1%
82
 
5.1%
103
 
7.7%
121
 
2.6%
ValueCountFrequency (%)
121
 
2.6%
103
 
7.7%
82
 
5.1%
72
 
5.1%
61
 
2.6%
42
 
5.1%
32
 
5.1%
24
 
10.3%
14
 
10.3%
018
46.2%

San Marino
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9487179487
Minimum0
Maximum12
Zeros32
Zeros (%)82.1%
Negative0
Negative (%)0.0%
Memory size440.0 B
2021-05-30T21:31:11.150215image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.2
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.480966683
Coefficient of variation (CV)2.61507299
Kurtosis10.5168217
Mean0.9487179487
Median Absolute Deviation (MAD)0
Skewness3.113326312
Sum37
Variance6.155195682
MonotonicityNot monotonic
2021-05-30T21:31:11.306430image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
032
82.1%
52
 
5.1%
11
 
2.6%
31
 
2.6%
41
 
2.6%
71
 
2.6%
121
 
2.6%
ValueCountFrequency (%)
032
82.1%
11
 
2.6%
31
 
2.6%
41
 
2.6%
52
 
5.1%
71
 
2.6%
121
 
2.6%
ValueCountFrequency (%)
121
 
2.6%
71
 
2.6%
52
 
5.1%
41
 
2.6%
31
 
2.6%
11
 
2.6%
032
82.1%

Serbia
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
0
36 
12
 
1
1
 
1
7
 
1

Length

Max length2
Median length1
Mean length1.025641026
Min length1

Characters and Unicode

Total characters40
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)7.7%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
036
92.3%
121
 
2.6%
11
 
2.6%
71
 
2.6%

Length

2021-05-30T21:31:11.684260image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-30T21:31:11.793615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
036
92.3%
121
 
2.6%
11
 
2.6%
71
 
2.6%

Most occurring characters

ValueCountFrequency (%)
036
90.0%
12
 
5.0%
71
 
2.5%
21
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number40
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
036
90.0%
12
 
5.0%
71
 
2.5%
21
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common40
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
036
90.0%
12
 
5.0%
71
 
2.5%
21
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII40
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
036
90.0%
12
 
5.0%
71
 
2.5%
21
 
2.5%

Spain
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
0
37 
4
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)5.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
037
94.9%
41
 
2.6%
21
 
2.6%

Length

2021-05-30T21:31:12.124948image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-30T21:31:12.263053image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
037
94.9%
41
 
2.6%
21
 
2.6%

Most occurring characters

ValueCountFrequency (%)
037
94.9%
41
 
2.6%
21
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number39
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
037
94.9%
41
 
2.6%
21
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common39
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
037
94.9%
41
 
2.6%
21
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII39
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
037
94.9%
41
 
2.6%
21
 
2.6%

Sweden
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.179487179
Minimum0
Maximum10
Zeros29
Zeros (%)74.4%
Negative0
Negative (%)0.0%
Memory size440.0 B
2021-05-30T21:31:12.372365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile5.3
Maximum10
Range10
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation2.41564849
Coefficient of variation (CV)2.048049806
Kurtosis4.803823382
Mean1.179487179
Median Absolute Deviation (MAD)0
Skewness2.245981022
Sum46
Variance5.835357625
MonotonicityNot monotonic
2021-05-30T21:31:12.528610image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
029
74.4%
43
 
7.7%
52
 
5.1%
11
 
2.6%
21
 
2.6%
31
 
2.6%
81
 
2.6%
101
 
2.6%
ValueCountFrequency (%)
029
74.4%
11
 
2.6%
21
 
2.6%
31
 
2.6%
43
 
7.7%
52
 
5.1%
81
 
2.6%
101
 
2.6%
ValueCountFrequency (%)
101
 
2.6%
81
 
2.6%
52
 
5.1%
43
 
7.7%
31
 
2.6%
21
 
2.6%
11
 
2.6%
029
74.4%

Switzerland
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.846153846
Minimum0
Maximum12
Zeros5
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size440.0 B
2021-05-30T21:31:12.688603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.5
median7
Q310
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation4.107364769
Coefficient of variation (CV)0.5999521572
Kurtosis-1.037942335
Mean6.846153846
Median Absolute Deviation (MAD)3
Skewness-0.3738740617
Sum267
Variance16.87044534
MonotonicityNot monotonic
2021-05-30T21:31:12.874791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
128
20.5%
76
15.4%
106
15.4%
05
12.8%
54
10.3%
84
10.3%
12
 
5.1%
21
 
2.6%
31
 
2.6%
41
 
2.6%
ValueCountFrequency (%)
05
12.8%
12
 
5.1%
21
 
2.6%
31
 
2.6%
41
 
2.6%
54
10.3%
61
 
2.6%
76
15.4%
84
10.3%
106
15.4%
ValueCountFrequency (%)
128
20.5%
106
15.4%
84
10.3%
76
15.4%
61
 
2.6%
54
10.3%
41
 
2.6%
31
 
2.6%
21
 
2.6%
12
 
5.1%

Ukraine
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.487179487
Minimum0
Maximum12
Zeros20
Zeros (%)51.3%
Negative0
Negative (%)0.0%
Memory size440.0 B
2021-05-30T21:31:13.031001image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile8.2
Maximum12
Range12
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.251491924
Coefficient of variation (CV)1.307300877
Kurtosis0.7123684499
Mean2.487179487
Median Absolute Deviation (MAD)0
Skewness1.193700458
Sum97
Variance10.57219973
MonotonicityNot monotonic
2021-05-30T21:31:13.187254image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
020
51.3%
54
 
10.3%
33
 
7.7%
12
 
5.1%
42
 
5.1%
62
 
5.1%
72
 
5.1%
21
 
2.6%
81
 
2.6%
101
 
2.6%
ValueCountFrequency (%)
020
51.3%
12
 
5.1%
21
 
2.6%
33
 
7.7%
42
 
5.1%
54
 
10.3%
62
 
5.1%
72
 
5.1%
81
 
2.6%
101
 
2.6%
ValueCountFrequency (%)
121
 
2.6%
101
 
2.6%
81
 
2.6%
72
5.1%
62
5.1%
54
10.3%
42
5.1%
33
7.7%
21
 
2.6%
12
5.1%

United Kingdom
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
0
39 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
039
100.0%

Length

2021-05-30T21:31:13.531986image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-30T21:31:13.641332image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
039
100.0%

Most occurring characters

ValueCountFrequency (%)
039
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number39
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
039
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common39
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
039
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII39
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
039
100.0%

Interactions

2021-05-30T21:29:47.124107image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:47.318471image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:47.505408image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:47.677248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:47.849117image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:48.021501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:48.193331image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:48.380790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:48.570792image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:48.742660image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:48.914462image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:49.088847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:49.263221image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:49.435611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:49.621581image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:49.777802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:49.949668image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:50.124001image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:50.280252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:50.452049image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:50.640201image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:50.812040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:50.999496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:51.158234image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:51.330028image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:51.517483image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:51.690846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:51.847065image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:52.018900image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:52.177652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:52.333868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:52.505702image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:52.695626image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:52.883087image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:53.054924image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:53.227279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:53.383475image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:53.539688image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:53.745281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:53.932740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:54.088952image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:54.252238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:54.403190image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:54.559404image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:54.731237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:54.921284image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:55.077501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:55.233713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:55.391408image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:55.547617image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:55.719450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:55.926359image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:56.098194image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:56.254410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:56.425808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:56.597646image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:56.753862image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:56.943800image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:57.162502image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:57.365580image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:57.524324image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:57.680536image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:57.836750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:58.010088image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:58.166305image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:58.322518image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:58.543757image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:58.715589image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:58.887459image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:59.060000image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:59.254106image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:59.410314image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:59.593327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:59.765200image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:29:59.905758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:00.091357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:00.292570image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:00.463231image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:00.643411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:00.799616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:00.955795image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:01.096385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:01.287362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:01.443606image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:01.615409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:01.787702image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:01.943953image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:02.100163image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:02.274464image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:02.461923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:02.649375image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:02.822671image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:02.968333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:03.124547image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:03.293959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:03.469018image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:03.640890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:03.825975image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:03.982156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:04.138401image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:04.341859image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:04.593848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:04.843642image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:05.019647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:05.238315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:05.423663image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:05.642591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:05.846728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:06.006490image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:06.162736image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:06.318914image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:06.476735image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:06.648576image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:06.804790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:06.993793image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:07.165635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:07.321848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:07.487511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:07.667096image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:07.823308image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:08.014237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:08.169554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:08.325768image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-05-30T21:30:54.718757image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:54.890590image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:55.046805image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:55.233699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:55.378323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:55.534537image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:55.690748image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:55.862347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:56.018557image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:56.190395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:56.364359image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:56.520576image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:56.676790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:56.867395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:57.023609image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:57.179822image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:57.354126image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:57.510345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:57.666559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:57.846009image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:58.011139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:58.167353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:58.323566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:58.496416image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:58.652634image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:58.808848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:58.968624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:59.159729image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:59.331568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:59.500315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:59.656500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:30:59.828334image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:31:00.049349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:31:00.221179image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:31:00.393013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:31:00.583969image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-30T21:31:00.740186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-05-30T21:31:13.893183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-30T21:31:14.459187image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-30T21:31:14.959582image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-30T21:31:15.477600image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-30T21:31:15.917545image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-30T21:31:01.257680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-30T21:31:02.547307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Country (Voters (vertical), Finalists (horizontal))AlbaniaAzerbaijanBelgiumBulgariaCyprusFinlandFranceGermanyGreeceIcelandIsraelItalyLithuaniaMaltaMoldovaNetherlandsNorwayPortugalRussiaSan MarinoSerbiaSpainSwedenSwitzerlandUkraineUnited Kingdom
0Albania0000731006004080000051001200
1Australia0602407008060120300100001050
2Austria0005018201206040307000001000
3Azerbaijan0031004010000078002120005060
4Belgium0006080003020500017000412100
5Bulgaria0000017080310051220600040000
6Croatia0000006001051223000140700800
7Cyprus0245037012008010000160000200
8Czechia0834011000806070001220000500
9Denmark7006080001010040030050021200

Last rows

Country (Voters (vertical), Finalists (horizontal))AlbaniaAzerbaijanBelgiumBulgariaCyprusFinlandFranceGermanyGreeceIcelandIsraelItalyLithuaniaMaltaMoldovaNetherlandsNorwayPortugalRussiaSan MarinoSerbiaSpainSwedenSwitzerlandUkraineUnited Kingdom
29Romania0002084100030126001000000750
30Russia0036200070510041200000000100
31San Marino2003011208001067500000000400
32Serbia0206010120372800000500004100
33Slovenia0560042031011205000080000700
34Spain0004601201730280005000001000
35Sweden0010006007410012002030000580
36Switzerland0301001120050846000730000000
37Ukraine0060001000471205001200000800
38United Kingdom0015041200106000000703020800